While arsenic has had broad industrial, agricultural, and medical applications, it is considered a highly toxic substance and its disposal is highly regulated. Arsenic is one of the most pervasive contaminants of drinking water in developing countries, as well as in the regions of the mid- and southwest United States, particularly near contaminated Superfund sites. It has been found to have various detrimental effects to humans in being classified as carcinogenic, immunocompromising, and deleterious to health. While there are methods established to measure arsenic in the field, they often require toxic substances (e.g. mercury) or generate even more hazardous waste (e.g. arsine gas). Current methods require arsenic measurements to be performed in laboratories, where large, bulky instruments are used to evaluate arsenic concentrations. On-site, frequent testing of arsenic can reduce the incident rates of arsenicosis, as well as to gauge the danger that arsenic contamination may pose to the ecosystem and nearby communities. While many colorimetric field kits are available, they require considerable work, time, and hazardous materials. Our goal at Cambrian Innovation is to create an accurate, field capable arsenic sensor with rapid response time that can identify arsenic contamination in groundwater/drinking water sources directly in the field while markedly improving on current field-based technologies. Our strategy would leverage the concepts of current glucose meter technology used with diabetes management and apply it to the detection of arsenic in water samples. While current methods of detecting arsenic require the generation of arsine gas or mercury halides, our proposed method would have a net zero generation of hazardous wastes, all the while providing a simple testing procedure and sensitive readings down to 10 ug/L or lower. We will achieve this by optimizing selection and isolation of arsenic specific enzymes, and evaluating their compatibility and response using chronoamperometric techniques through a series of algorithms and signal processing. The work proposed here in Phase I will provide feasibility data on utilization and sensitivity of arsenic specific enzymes to detect concentrations in the form of an enzymatic test strip.